Hyperspectral Estimation Models for the Saline Soil Salinity in the Yellow River Delta

摘要

Quantitative identification of the saline soil salinity content is a necessary precondition for the reasonable improvement and utilization of saline land, the article aimed at comparing the different quantitative analysis methods and achieving fast estimation of the saline soil salt content in the Yellow River Delta based on the visible-near infrared spectroscopy. Kenli County in Shandong Province was selected as the experimental area, firstly, the representative soil samples were selected, hyperspectral reflectance of the soil samples were measured in situ and transformed to the first deviation. Secondly the correlate spectra, the characteristic spectra and indices were firstly filtered using correlation analysis. Finally, the estimation models of soil salinity content were built using the multiple linear regression (MLR), back propagation neural network(BPNN) and support vector machine(SVM) respectively. The results indicated that the characteristic wave bands of soil salinity were 684 nm and 2058 nm. On the condition of the same input variables, the prediction precision of the SVM models was the highest, followed by the BPNN, the MLR was the lowest. The SVM model based on the first deviation of the reflectance at 684 and 2058nm had the highest precision, with the calibration R~2 of 0.91 and RMSE as 0.11%, the validation R~2 of 0.93, RMSE as 0.26% and RPD as 2.61, which had very good prediction accuracy of soil salt content, and was very stable and reliable. Different input variables had a great impact on the model accuracy, among of the MLR models, only the precision of the model based on characteristic spectral indices was slightly higher and could be used to estimate salt content, among of the BPNN and SVM models, the precision of the models based on characteristic spectra and indices was more high and stable significantly than the models on the correlate spectra. Therefore, for the three modeling methods of multiple linear regression, back propagation neural network and support vector machine, building the estimation model of saline soil salinity content based on characteristic spectra indices was effective.